For the serious quantization error in vector quantitation coding,the sparse coding is only a shallow learning model which caused the codeword lack selectivity for image features.In this paper,an image classification method based on deep learning coding model was proposed.Firstly,the deep learning network unsupervised RBM was used to encode SIFT features and generate visual dictionary instead of the traditionalK-means clustering.Then,the unsupervised RBM learning was steered by using a regularization scheme,which decomposes into a combined prior for the sparsity of each feature's representation as well as the selectivity for each codeword.Finally,the initial dictionary was fine-tuned to be discriminative through the supervised learning from top-down labels.To train SVM classifier and complete image classification,the representation features based on image deep learning were obtained.The experimental results demonstrated that the proposed method can overcome the disadvantage of vector quantization coding and sparse coding.Moreover,the classification performance can be boosted effectively.
